OLAP Data Research Articles

PERANCANGAN DASHBOARD UNTUK PEMANTAUAN TERHADAP KEBUTUHAN PEMBELIAN MATERIAL PADA PT XYZ MENGGUNAKAN METODE OLAP

This XYZ company is engaged in Agribusiness. In the process of coconut processing palm oil needs the help of materials such as iron, steel, and so on. The data to be processed from this company is material purchasing data. Of course, processing this data requires an application from the business intelligence or commonly abbreviated as Business Intelligence or BI. Deployed BI application is a dashboard to display the amount of material purchases along with targets, Order Unit, Category, Location, and Date. The process is created with perform ETL processing on OLTP, and OLAP data. Then will make dashboard using Power BI which is then uploaded to the website so that it can be accessed by users online. In this research method using the OLAP method. This OLAP method aims to analyze deeper data regarding the year, month, and date. Analyze in more detail using drilldown. The drilldown is used in order to be able to analyze in detail the amount of material purchase requirements which will be analyzed in more depth regarding the achievement of material purchasing needs. The final result of this research is a dashboard. This can be used as a reference by the user for material monitoring and analysis

Формирование данных информационной модели модульного здания в формате olap

The article provides an analysis of the use of OLAP technology in the design of modular objects based on information modeling technologies, presents the classification and systematization of information models of modular elements of maximum readiness. The purpose of this study is to develop and analyze a data model for the most effective data management of an information model of a capital construction object both at the design stage and at subsequent stages of the life cycle. In order to achieve this goal, it is necessary to take into account not only the features and attributes of information models, but also the key characteristics of modular design, including elements of the MEG within the existing tools for systematization of digital data. As a result, the impact of using OLAP cubes to represent a modular object at all stages of the life cycle is considered. New possibilities and advantages of using the OLAP data management model of the information model of the capital construction object are presented. The directions of further research are formulated.

Development of Business Intelligence For Integration of Financial Reporting System In PT XYZ

The research aims to determine the relationship between two or more variables. Data collection with Literature and field research with questionnaires. The result of the research that is more influential in BAPPEDA Pemprov DKI Jakarta is the Business Intelligence variable, so it needs to be prioritized and given special attention to its implementation in the field. The research method used is associative research, designing Data Warehouse architecture, planning Data Warehouse sources, dimensional data modeling, filling in Data Warehouse. And the results of this study can be used as a reference for further follow-up with the DKI Jakarta Provincial Government BAPPEDA to be prioritized in the near future. The conclusion from the results of this study is that a transaction data warehouse designed using PDI (Pentaho Data Integration), is very helpful in collecting transaction data from original transaction data that already has a database and only as OLTP (Online Transactional Processing) data so that can be used as data OLAP. (Online Analysis Processing) so that it is analyzed using OLAP.

Analysis and Identification of International Tourist Visits to Indonesia Based on Data Warehouse Implementation

Foreign tourist visits to Indonesia play an important role in the economic growth of the tourism sector. In increasing the interest of visitors, development resources are needed as a tourism sector. The development carried out must be carried out optimally with the growth trend of tourist visits so that development is right on target, effective and efficient. This study aims to visit foreign tourists to Indonesia using the implementation of a data warehouse. The dataset used is data on foreign tourist arrivals by nationality in 2021 sourced from the Central Statistics Agency (BPS). This is done to determine the interest and potential of tourism to support the country's economy. The steps taken in this analysis and identification are Choosing the Process, Choosing the Grain, Identifying and confirming the dimension, Choosing The Facts, Rounding out the dimension table, Choosing the duration of database, Tracking Slowly changing Dimensions, Deciding the query priorities and the query mode, and the last Storing pre-calculation in the fact information. The result can be seen Foreign Tourism Visits in Indonesia using the Application of OLAP Data Warehouse for analysis. So that in 2021 it can be analyzed that monthly tourist visits tend to be stable with the periods of July-August and January-December increasing. These results in the future can be used as a policy reference in increasing tourist visits to foreign tourist targets in certain countries. This is done to determine the interest and potential of tourism to support the country's economy through the tourism sector

Efficiency Analysis of OLAP-data Hypercube Decomposition for Exponential Computational Complexity Methods

The paper studies problems of reduction (decomposition) of OLAP-hypercube multidimensional data models. When decomposing large hyper-cubes of multidimensional data into sub-cube components the goal is to increase the computational performance of analytical OLAP systems, which is related to decreasing computational complexity of reduction methods for solving OLAP-data analysis problems with respect to the computational complexity of non-reduction methods, applied to data directly all over the hypercube. The paper formalizes the concepts of reduction and non-reduction methods and gives a definition of the upper bound for the change in the computational complexity of reduction methods in the decomposition of the problem of analyzing multidimensional OLAP-data in comparison with non-reduction methods in the class of exponential degree of computational complexity.The exact values of the upper bound for changing computational complexity are obtained for the hypercube decomposition into two sub-cubes on sets consisting of an even and an odd number of sub-cube structures, and its main properties are given, which are used to determine the decomposition efficiency. A formula for the efficiency of decomposition into two sub-cube structures for reduction of OLAP data analysis problems is obtained, and it is shown that with an increase in the dimension “n” of the lattice specifying the number of sub-cubes in the hypercube data structure, the efficiency of such a decomposition obeys an exponential law with an exponent “n/2”, regardless of the parity “n”. The examples show the possibility to use the values (found) of the upper bound for the change in computational complexity to establish the effectiveness criteria for reduction methods and the expediency of decomposition in specific cases.The paper results can be used in processing and analysis of information arrays of hypercube structures of analytical OLAP systems belonging to the Big-Data or super-large computer systems of multidimensional data.

Computational Complexity Analysis of Decomposition Methods of OLAP Hyper-cubes of Multidimensional Data

The paper investigates the problems of reduction (decomposition) of multidimensional data models in the form of hypercube OLAP structures. OLAP data processing does not allow changes in the dimension of space. With the increase in data volumes, the productivity of computing cubic structures decreases. Methods for reducing large data cubes to sub-cubes with smaller volumes can solve the problem of reducing computing performance.The reduction problems are considered for cases when the cube lattice has already determined criteria aggregation, and the cube decomposition into smaller cubes is needed to reduce the computation time of the full lattice when dynamically changing data in the cube.The objective of the paper is to find conditions for reducing the computational complexity of solving data analysis problems by reduction methods, to obtain exact quantitative boundaries for reducing the complexity of decomposition methods from the class of polynomial degrees of complexity, to establish the nature of the dependence of computational performance on the structural properties of a hypercube, and to determine the quantitative boundaries of computational performance for solving decomposition problems of data aggregation .The study of the computational complexity of decomposition methods for the analysis of multidimensional hyper-cubes of polynomial-logarithmic and polynomial degrees of complexity is carried out. An exact upper limit is found for reducing the complexity of decomposition methods for analyzing the initial OLAP - data hypercube with respect to non-decomposition ones and based on them criteria are proved for the effective application of reduction methods for analyzing hypercube structures in comparison with traditional non-reduction methods.Examples of decomposition methods of cube structures are presented, both reducing and increasing computational complexity in comparison with calculations using the full model.The results obtained can be used in processing and analysis of information arrays of hypercube structures of analytical OLAP-systems belonging to the BigData class, or ultra-large computer multidimensional data systems.

A New Big Data Benchmark for OLAP Cube Design Using Data Pre-Aggregation Techniques

In recent years, several new technologies have enabled OLAP processing over Big Data sources. Among these technologies, we highlight those that allow data pre-aggregation because of their demonstrated performance in data querying. This is the case of Apache Kylin, a Hadoop based technology that supports sub-second queries over fact tables with billions of rows combined with ultra high cardinality dimensions. However, taking advantage of data pre-aggregation techniques to designing analytic models for Big Data OLAP is not a trivial task. It requires very advanced knowledge of the underlying technologies and user querying patterns. A wrong design of the OLAP cube alters significantly several key performance metrics, including: (i) the analytic capabilities of the cube (time and ability to provide an answer to a query), (ii) size of the OLAP cube, and (iii) time required to build the OLAP cube. Therefore, in this paper we (i) propose a benchmark to aid Big Data OLAP designers to choose the most suitable cube design for their goals, (ii) we identify and describe the main requirements and trade-offs for effectively designing a Big Data OLAP cube taking advantage of data pre-aggregation techniques, and (iii) we validate our benchmark in a case study.

Personel İş Zekası Sistemi ve Veri Madenciliği ile Personel Memnuniyetinin Ölçülmesi

Veri madenciliği uygulamaları ve iş zekası sistemleri verileri anlamlandırarak bilgiye dönüştürülmesinde ve kuruluşlara karar verme süreçlerinde yardımcı olmak için kullanılır. Çalışmada bir devlet kurumunun personel verileri üzerinde iş zekası ve veri madenciliği süreçleri gerçekleştirilerek verilerin yönetimini sağlamak ve dinamik analizler ile stratejik öngörülerin yapılabilmesi amaçlanmıştır. İş zekası süreçlerinin uygulanmasında Microsoft iş zekası araçlarından SSIS (SQL Server Integration Service), SSAS (SQL Server Analysis Service) ve Power BI kullanılmıştır. Öncelikle veri yapısı modellendirilmiş olup ETL (Extract-Transform-Load) sürecinden geçirilerek anlamlı hale getirilen veri, veri ambarına SSIS programı ile aktarılmıştır. Daha sonra SSAS programı ile OLAP veri küpü oluşturulmuştur. Son olarak da OLAP küpü veri kaynağı olarak belirlenip zengin görsel araçlara sahip olan ve veri analizini çok daha etkili hale getiren Power BI iş zekası aracı kullanılarak çeşitli görsellerle veri analizi gerçekleştirilmiştir. Böylece, karar vericilerin veri analizini etkin bir şekilde yapabilecekleri bir karar destek sistemi geliştirilmiştir. Çalışmada kümeleme analizi yöntemlerinden biri olan K-Means algoritması ve birliktelik kurallarından kurallarından Apriori algoritması ile bir devlet kurumundaki İç Kontrol Sistemi anketi verileri kullanılarak personel memnuniyet analizi yapılmıştır. Veri madenciliği aracı olarak ise içerisinde bulundurduğu bir çok algoritma sayesinde verilerden bilgi çıkarımı yapılabilmesine olanak sağlayan ve açık kaynak kodlu bir araç olması sebebi ile WEKA kullanılmıştır. Kümeleme analizi sonucunda oluşan farklı personel grupları detaylı incelenerek bu gruplara nasıl yaklaşılması gerektiği ve nasıl hitap edilmesi gerektiği belirlenmiştir. Her grubun memnuniyet durumunun farklılaşan noktaları belirlenerek, stratejiler ve uygulama faaliyetleri bu grubun ihtiyacına göre planlanmıştır. Apriori algoritması, personelin İç Kontrol Sistemi anket sorularına vermiş olduğu cevaplar arasında anlamlı ilişkileri bulmak, bu ilişkilerden faydalanarak personelin çalıştığı birimden memnuniyetini karşılaştırarak anlamlı sonuçlar elde etmek ve çalışılan birimden memnun olmama nedenlerini keşfetmek için kullanılmıştır. Böylece İç Kontrol Sistemi anketi verilerinden anlamlı ve yararlı bilgiler elde etmek ve bu bilgiler ışığında personel karar destek planlama faaliyetlerinde destek olmak amaçlanmıştır.

Strategic Importance of Knowledge Management in Compananies and Formation of Olap Data Analysis Model Reports and Examples

Bu çalışmamızda, olap teknolojilerinin işletmeler açısından stratejik önemi vurgulanmakta ve olap araçlarının kullanım yöntemleri incelenmektedir. İşletmelerde talebin doğru ve hızlı yönetiminde olap modelinin etkinliğini literatürdeki tartışmalar ve uygulamadan örnekler ile gösterilmesi amaçlanmıştır. Ham veriyi işleyerek, anlamlı ve stratejik bilgiye dönüştüren, olap olarak bilinen çok boyutlu analiz sistemlerinin, yöneticilere, hızlı ve doğru kararlar vermede, önemli ve faydalı bir çözüm sunduğu tespit edilmiştir. Bütün tedarik zinciri boyunca doğru bilgiye ve hızlı ulaşabilmek, yöneticilere büyük yararlar sağlar. Olap çok boyutlu analiz ve karar destek için veri ambarlarına erişimi sağlar. Eğilimlerin belirlenmesi veya müşteri isteklerinin ortaya çıkarılması gibi çoğu işletmenin beklentisi olan depolanmış bilgiden yeni bilgiler türetmeyi de sağlamaktadır. Araştırmamızda, uygulamadan örnek olarak perakende sektöründen bir işletmede talep ve bilgi yönetiminde verilerin çok boyutlu olarak analiz edilmesine yönelik kullanılan raporlar gösterilmiştir. İşletmelerde aktif ve yaygın olarak olap modelinin kullanıldığı tespit edilmiş ve modelin oluşturulması ve uygulama örnekleri de sunulmuştur. Olap modelinin, yöneticiler tarafından etkin olarak kullanıldığı ve işletmeye katkılar sağladığı tespit edilmiştir. İşletmelerin veri yığınları karşısında anlamlı sonuçlara en kısa sürede ulaşmalarını sağlayacak olap gibi karar destek modellerine ihtiyaçları vardır.

SPPOLAP: Computing Privacy-Preserving OLAP Data Cubes Effectively and Efficiently Algorithms, Complexity Analysis and Experimental Evaluation.

This paper provides significant contributions in the line of the so-called privacy-preserving OLAP research area, via extending the previous SPPOLAP’s results provided recently. SPPOLAP is a state-of-the-art algorithm whose main goal consists in computing privacy-preserving OLAP data cubes effectively and efficiently. The main innovations carried-out by SPPOLAP are represented by the novel privacy OLAP notion and the flexible adoption of sampling-based techniques in order to achieve the final privacy-preserving data cube. In line with the main SPPOLAP’s results, this paper significantly extends the previous research efforts by means of the following contributions: (i) complete algorithms of the whole SPPOLAP algorithmic framework; (ii) complexity analysis and results; (iii) comprehensive experimental analysis of SPPOLAP against real-life multidimensional data cubes, according to several experimental parameters. These contributions nice-fully complete the state-of-the-art SPPOLAP’s results.

TeksDB

Key-value stores (KVS) are now an integral part of modern dataintensive systems. thanks to its simplicity, scalability, and efficiency over traditional database systems. Databases such as MySQL employ KVS (in this case, RocksDB as their backend storage instead of conventional database engines because KVS is less affected by data fragmentation. OLAP and big data analytics applications often use KVS as the backend store to make use of the efficient range_query operations of LSM(Log Structured Merge Tree)-based KVS. Furthermore, the increasing use of KVS as backend stores in distributed systems diversifies the workload for each local KVS, making it necessary to design a highly-concurrent data store that simultaneously processes mixed operations. The underlying technology trends also put immense pressure on the design of KVS. Nowadays, many core servers with hundred GBs of memory are common in production environments, and this enhanced density of hardware technologies (both in terms of the number of cores and memory capacity) leads to more data items residing in memory. This phenomenon places greater emphasis on the efficiency of the in-memory data structure in KVS, as more data accesses are serviced from its memory component, which is often called the memtable. The current memtable structure of KVS, however, generally assumes a limited memory capacity that only serves as a temporary write buffer, and the use of single-threaded writes or unscalable data structures for the memtable fails to fulfill this external demand.

Usability testing of SOLAP for Indonesia agricultural commodity

Spatial Online Analytical Processing (SOLAP) for Indonesia Agricultural Commodities has successfully developed in our previous study. The application is integrated into a data warehouse that manages historical agricultural commodities data in four sectors: food crop, horticulture, plantation, and livestock. The main features of SOLAP are providing historical data as the results of roll up and drill down operation and visualization of the data in crosstab tables, graphs, and map. In order to evaluate the SOLAP implementation, this study conducted usability testing by involving respondents as potential users of SOLAP. We use the Post-Study System Usability Questionnaire (PSSUQ) which tests the system based on efficiency, effectivity, and user’s satisfaction. There are three testing aspects in PSSUQ, namely system usefulness, information quality, and interface quality with the Likert scale from 1 (low) to 7 (high). The measurement was done by involving 13 respondents who are postgraduate students of Bogor Agricultural University (IPB), researchers in Agrarian Study Center IPB, data analysis and data management staff of Data and Information Center, Ministry of Agriculture Indonesia. The results show that SOLAP for Indonesia Agricultural Commodities has high usability measures which reach 5.424. The testing results of each aspect are system usefulness of 5.54, information quality of 5.258, and interface quality of 5.433. In addition to PSSUQ, this study performed usability testing based on tasks completion. The result shows that the respondents are able to complete the OLAP operations and data visualization in an acceptable time.

Intelligent Data Analysis in the EMERCOM Information System

The paper describes an information system development project for the Russian Ministry of Emergency Situations (MES, whose international operations body is known as EMERCOM), which was attended by the representatives of both the IT industry and the academia. Besides the general description of the system, we put forward OLAP and Data Mining-based approaches towards the intelligent analysis of the data accumulated in the database. In particular, some operational OLAP reports and an example of multi-dimensional information space based on OLAP Data Warehouse are presented. Finally, we outline Data Mining application to support decision-making regarding security inspections planning and results consideration.

Giving Intelligence to SMEs Business

Few decades onwards decision-making systems have underwent many changes with the new technologies of Data warehouse/Data Mining. The tendency in OLAP data plays an important role on Business Intelligence over OLTP data, i.e., day to day data. Business Intelligence is not just for big organizations, but it is a simple technology that can be used by small and medium enterprises (SMEs) too for better results in their businesses. The main intention of this paper is to describe the usage of BI and how it is useful to the organizations in decision making. BUSINESS INTELLIGENCE (Bl) is one of the simplest technology to understand. However, getting it into the right direction is the most difficult tasks for both small and large companies. In simple terms, Bl is all about taking the volume of data that every company collects on daily basis and present it in a form that can be understood by the decision makers. The ability to use historical information to make business decisions are the fundamental basis of a business. Transforming data into information and bringing together disparate sources of valuable information to analyse BI can enable SMEs to gain timely access to high quality, reliable business data and metrics. That enables them to make better-informed decisions while dealing with customers, suppliers or internal processes. Fundamentally, BI enables companies to more easily identify and reduce the cost and also to take guesswork out of decisions that help drive top-line revenue growth.

Thuật toán tính tổng theo miền trong xử lý phân tích trực tuyến đối với các kho dữ liệu

A range query applies an aggregation operation over all selected cells an OLAP data cube where the selection is specified by providing ranges of values for numeric dimensions. We present fast algorithm for range queries for the most popular aggregation operation: SUM.

Explanation in OLAP Data Cubes

OLAP is an important technology that offers a fast and interactive data navigation, it also provides tools to explore data cubes in order to extract interesting information from a multidimensional data structures. However, the OLAP exploration is done manually, without tools that could automatically extract relevant information from the cube. In addition OLAP is not capable of explaining relationships that could exist within data. This paper presents a new approach to coupling between data mining and online analytical processing. Its approach provides the explanation in OLAP data cubes by using the association rules between the inter-dimensional predicates. The mining process could be done by one of the two algorithms, Apriori and Fp-Growth, in which aggregate measures to calculate support and confidence are exploited. It also evaluates the interestingness of mined association rules according to the Lift criteria.

A Decomposition Framework for Computing and Querying Multidimensional OLAP Data Cubes over Probabilistic Relational Data

Focusing on novel database application scenarios, where data sets arise more and more in uncertain and imprecise formats, in this paper we propose a novel decomposition framework for efficiently computing and querying multidimensional OLAP data cubes over probabilistic data, which well-capture previous kind of data. Several models and algorithms supported in our proposed framework are formally presented and described in details, based on well-understood theoretical statistical/probabilistic tools, which converge to the definition of the so-called probabilistic OLAP data cubes, the most prominent result of our research. Finally, we complete our analytical contribution by introducing an innovative Probability Distribution Function PDF-based approach, which makes use of well-known probabilistic estimators theory, for efficiently querying probabilistic OLAP data cubes, along with a comprehensive experimental assessment and analysis over synthetic probabilistic databases.

Explanatory Business Analytics in OLAP

In this paper the authors describe a method to integrate explanatory business analytics in OLAP information systems. This method supports the discovery of exceptional values in OLAP data and the explanation of such values by giving their underlying causes. OLAP applications offer a support tool for business analysts and accountants in analyzing financial data because of the availability of different views and managerial reporting facilities. The purpose of the methods and algorithms presented here, is to extend OLAP applications with more powerful analysis and reporting functions. The authors describe how exceptional values at any level in the data, can be automatically detected by statistical models. Secondly, a generic model for diagnosis of atypical values is realized in the OLAP context. By applying it, a full explanation tree of causes at successive levels can be generated. If the tree is too large, the analyst can use appropriate filtering measures to prune the tree to a manageable size. This methodology has a wide range of applications such as interfirm comparison, analysis of sales data and the analysis of any other data that possess a multi-dimensional hierarchical structure. The method is demonstrated in a case study on financial data.

Modeling and Querying Continuous Fields with OLAP Cubes

The notion of SOLAP (Spatial On-Line Analytical Processing) is aimed at exploring spatial data in the same way as OLAP operates over tables. SOLAP, however, only accounts for discrete spatial data. Current decision support systems are increasingly being needed for handling more complex types of data, like continuous fields, which describe physical phenomena that change continuously in time and/or space (e.g., temperature). Although many models have been proposed for adding spatial (continuous and discrete) information to OLAP tools, no one is general enough to allow users to just perceive data as a cube, and analyze any type of spatial data together with typical alphanumerical discrete OLAP data, using only the classic OLAP operators (e.g., Roll-up, Drill-down). In this paper the authors propose a model and an algebra supporting it, that allow operating over data cubes, independently of the underlying data types and physical data representation. That means, in this approach, the final user only sees the typical OLAP operators at the query level, whereas at lower abstraction levels the authors provide discrete and continuous spatial data support as well as different ways of partitioning the space. As far as the authors are aware of, this is the first proposal, which provides such a general framework for spatiotemporal data analysis.

A relational framework and case shells of a new type

This paper describes a CASE shell of a new type, namely, the instrumental system SWS constructed in accordance with a framework data model. A scheme of the instrumental system is proposed. The list of main user functions of the SWS shows that the typification and formalization of requests to a database with a well-formalized data model and unification and minimization of the interface allow modelling the totality of user requests. The results of a numerical experiment on the real-time formation of OLAP data elements are presented. The real-time approach is shown to be most advanced.